Simple example of a Machine Learning (ML) model

 Here’s a simple example of a Machine Learning (ML) model using Python and Scikit-Learn to predict house prices using Linear Regression.

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📌 Step 1: Install Required Libraries

If you haven’t installed scikit-learn and pandas, install them first:

bash

pip install scikit-learn pandas numpy matplotlib

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📌 Step 2: Import Libraries

python

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, mean_squared_error

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📌 Step 3: Create Sample Data

python

# Creating a small dataset (house size vs. price)
data = {
    "House_Size": [750, 800, 850, 900, 950, 1000, 1050, 1100, 1150, 1200],
    "Price": [150000, 160000, 170000, 180000, 190000, 200000, 210000, 220000, 230000, 240000]
}

df = pd.DataFrame(data)
print(df.head())  # Display first few rows

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📌 Step 4: Split Data into Training & Testing Sets

python

X = df[["House_Size"]]  # Features (Input)
y = df["Price"]  # Target (Output)

# Splitting data into 80% training and 20% testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

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📌 Step 5: Train the ML Model (Linear Regression)

python

# Initialize and train the model
model = LinearRegression()
model.fit(X_train, y_train)

# Print model parameters
print(f"Intercept: {model.intercept_}")
print(f"Coefficient: {model.coef_[0]}")

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📌 Step 6: Make Predictions

python

y_pred = model.predict(X_test)

# Display actual vs predicted values
comparison = pd.DataFrame({"Actual": y_test, "Predicted": y_pred})
print(comparison)

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📌 Step 7: Evaluate Model Performance

python

mae = mean_absolute_error(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
rmse = np.sqrt(mse)

print(f"Mean Absolute Error (MAE): {mae}")
print(f"Mean Squared Error (MSE): {mse}")
print(f"Root Mean Squared Error (RMSE): {rmse}")

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📌 Step 8: Visualize the Model

python

plt.scatter(X, y, color="blue", label="Actual Data")
plt.plot(X, model.predict(X), color="red", label="Prediction Line")
plt.xlabel("House Size (sq ft)")
plt.ylabel("Price ($)")
plt.title("House Price Prediction")
plt.legend()
plt.show()

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🔹 Expected Output

• The linear regression model will fit a straight line through the data.

• It will predict house prices based on their size.

• You’ll get evaluation metrics like MAE, MSE, RMSE to measure accuracy.

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🚀 Want to Try a More Advanced Model?

• Replace Linear Regression with Decision Trees or Random Forest.

• Use a larger dataset like Boston Housing Dataset.

• Deploy the model using Flask or FastAPI.